The components of interaction and pre-processing are available for all integrated applications giving a researcher (whether clinical or computational) a single point of entry and exit for all their tasks.

The following sections give a high-level overview of CaPTk, with the intention of familiarizing a new user with the platform functionalities and interface.

Frequently Asked Questions

Supported Images

Currently, CaPTk supports visualization of MR, CT, PET, X-Ray and Full-field Digital Mammography (FFDM) images in NIfTI (i.e., .nii, .nii.gz) format. DICOM (i.e., .dcm) support is limited to a few protocols for MR, CT and MG modalities. In NIfTI format, for MRI, the following modalities are currently supported (the image modality box equivalent is shown in parenthesis):

Acronym

Image Type

T1

Native T1-weighted MR Image

T1Gd

Post-contrast T1-weighted MR Image (also known as T1c, T1-CE)

T2

Native T2-weighted MR Image

FLAIR

T2-weighted Fluid Attenuated Inversion Recovery MR Image

DSC-ap-rCBV

automatically-extracted proxy to relative Cerebral Blood Volume

PERFUSION

Dynamic Susceptibility Contrast-enhanced MR Image (also known as DSC)

DTI

Diffusion Tensor Image

DWI

Diffusion Weighted Image

REC

Recurrence Maps

* These modalities are currently visualized only within the "Confetti" application.

In addition, CaPTk offers the ability to extract and visualize commonly used measurements from DTI [1] and DSC-MRI, while accounting for leakage correction [2]. The exact measurements supported are:

Image & Mask Loading

The File -> Load -> Images menu is used to load all image types. Images can also be loaded by dragging and dropping onto the application. In case of DICOM images, loading/dragging a single image loads the entire dicom series.
Mask must be loaded via File -> Load -> ROI menu.

Loading images & mask for visualization/processing

For every loaded NIfTI image, the modality is automatically assigned using information from the filename. To assign modalities for DICOM images, or in case of a discrepancy in the modality of a NIfTI image, the user can use the drop-down modality switcher (see below) to revise the modality accordingly. This is typically needed in the Specialized Applications.

Modality switching for individual images

Image Visualization

Sliders on each visualization panel (highlighted in green in the figure below) control the movement across respective axes. Note that in DSC-MRI, the single horizontal slider (highlighted in yellow) moves across the time of the acquired dynamic scan.
Various adjustments are available to the user, including:

Manual Window/Level and setting of different visualization presets: bottom-right panel.

Image zoom: Ctrl + mouse wheel.

Image pan: Ctrl + left mouse button press + mouse move.

The bottom-left panel of CaPTk (highlighted in red) shows basic information about the image and the position of the cross-hair. In the visualization panes, the "Z" axis is the center; and "Y" and "X" are to its left and right, respectively. "Z" represents the Axial view for RAI-to-LPS images.

Visualization Sliders and Image Information

The "Overlay" functionality enables the visualization between two loaded images:

Double-click one of the two images you want to visualize and then click on its corresponding "overlay" radio button, on its right-most column.

Click on the "overlay" radio button of the second image that you want to visualize.

Both "Overlay" and "Underlay" images are now visualized with 50% opacity.

Check the tick box "Change Opacity".

Moving the slider changes the opacity between the "Overlay" and the "Underlay" images.

The "Comparison Mode" puts the loaded images (3 maximum) side-by-side for easy visualization along the same axis.

Comparison Mode visualization

Tab Docking

Double clicking on the tab bar will dock/undock the entire section, resulting in larger visualization panels in your monitor. This behavior is replicated by single click of the dock/undock button at the end of the section as well.

Left is while dock is active and right is undocked behavior

Coordinate definition (Seed-point initialization)

The Seed Points tab includes two general types of initialization (i.e., tumor and tissue points), controlled by the respective radio buttons.

Tumor Points

These are seed-points used to approximate a tumor by a parametric spherical model, using a seed-point for its center and another for defining its radius. These are helpful for applications like tumor growth model estimation as currently incorporated in GLISTR and GLISTRboost. The controls are as follows:

Tissue Points

These are seed-points with coordinate information. They can be used for a multitude of applications where manual initialization(s) are required for a semi-automated algorithm, e.g, segmentation. At the moment these points are being assigned various brain tissue labels, as follows:

Tissue Acronym

Full Form

CSF

Cerebrospinal Fluid

VT

Ventricular Cerebrospinal Fluid

GM

Gray Matter

WM

White Matter

VS

Vessels

ED

Edema

NCR

Necrosis

ET

Enhancing Tumor

NE

Non-Enhancing Tumor

CB

Cerebellum

CAE

Enhancing Cavity

CAN

Non-Enhancing Cavity

RTN

Tumor Recurrence

RTE

Enhanced Tumor Recurrence

Application-specific tissue types are automatically enabled when the corresponding application is selected. For example, when trying to initialize tissue points for GLISTR/GLISTRboost, only CSF, GM, WM, VS, ED, NCR, TU, NE and CB buttons will be enabled and the rest will be disabled. If there are some required tissue types missing for an application, CaPTk will display a warning and not let the user save an incomplete set of tissue points.

Utilities (Command-line only)

To make pipeline construction using CaPTk easier, a bunch of utilities have been provided. They include

Resizing

DICOM conversion

Sanity checking

Image header information

Unique values in image

Changing pixel values.

For full details, run the command:

Utilities.exe -u

Pre-processing

Image pre-processing is essential to quantitative image analysis. CaPTk pre-processing tools available under the "Preprocessing" menu are fully-parameterizable and comprise:

-Denoising. Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does NOT call out to the original implementation distributed by FSL. -Co-registration. Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5]. -Bias correction. Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2]. -Intensity normalization. Conversion of signals across modalities to comparable quantities using histogram matching [4]. -Z-Scoring normalization. Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6]. -Histogram Matching -Skull Stripping (Deep Learning based) -Mammogram Pre-processing

NOTE: An extended set of algorithms are available via the command line utility Preprocessing. For full details, run the command:

Feature Extraction

The feature extraction tab in CaPTk enables clinicians and other researchers to easily extract feature measurements, commonly used in image analysis, and conduct large-scale analyses in a repeatable manner.

Feature Panel screenshot

Although the feature panel in CaPTk is continuously expanding, it currently comprises i) intensity-based, ii) textural (GLCM, GLRLM, GLSZM, NGTDM, LBP), and iii) volumetric/morphologic features. You can find the exact list of features incorporated in CaPTk, together with their mathematical definition in the corresponding How-To section.

Specialized applications in CaPTk, such as "EGFRvIII Surrogate Index", "Survival Prediction", "Recurrence Estimator", and "SBRT-Lung" use features of this panel. However, the general idea is to keep the features generic and adaptable for different types of images by just changing the input parameters. Currently we provide some pre-selected parameters for Neuro and Torso images (i.e., Brain, Breast, Lung). Users can alter these pre-selected values through the Custom menu option, or create their own set of parameters via the Advanced menu. The output of the feature extraction tab is a text (.csv) file, with feature names and values. Note that the reported features are extracted per modality, per annotated region and per offset (offset represents the radius around the center pixel; for radius 1, the offset will be +/- 1) value.

Specialized Applications

Various Specialized Applications are currently incorporated in CaPTk focusing on brain tumors, breast cancer, and lung nodules as shown in the figure below.

Command Line Usage

Almost every application offered by CaPTk can be used from the command line and utilizing the CaPTk's powerful command line APIs is relatively straight-forward. The following parameters are present in every CLI:

Laconic

Verbose

Function/Action

u

usage

Usage message giving all the possible parameters of an application

h

help

Help message giving details of parameters (including defaults and ranges) with some examples

v

version

The version information of the application

An example to show the verbose help for an application called ${Application}: